U.S. patent application number 14/983629 was filed with the patent office on 2017-07-06 for methods and analytics systems having an ontology-guided graphical user interface for analytics models.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Tsuyoshi Ide, Jayant Kalagnanam, Young M Lee, Nizar Lethif, Naihui Song.
Application Number | 20170192957 14/983629 |
Document ID | / |
Family ID | 59235523 |
Filed Date | 2017-07-06 |
United States Patent
Application |
20170192957 |
Kind Code |
A1 |
Ide; Tsuyoshi ; et
al. |
July 6, 2017 |
METHODS AND ANALYTICS SYSTEMS HAVING AN ONTOLOGY-GUIDED GRAPHICAL
USER INTERFACE FOR ANALYTICS MODELS
Abstract
Embodiments include methods, and computer program products of an
ontology-guided analytics graphical user interface (GUI). Aspects
include: linking the ontology-guided graphical user interface for
analytics models to an ontology based analytics solution library,
receiving a definition of a management project to be solved by the
analytics system from a user via the ontology-guided graphical user
interface for analytics models, selecting, by the user via the
ontology-guided graphical user interface for analytics models, a
solution from the ontology based analytics solution library
according to the management project, building one or more workflows
to solve the management project, and executing the workflows to
generate solution to management project. Building may include
generating workflows of modeling tasks for analytics selected as an
actionable widget. Executing may include a representational state
transfer (REST)-based wrapper service including data store services
module, execution services module, analytics services module,
visualization services module, and data transformation services
module.
Inventors: |
Ide; Tsuyoshi; (Harrison,
NY) ; Kalagnanam; Jayant; (Tarrytown, NY) ;
Lee; Young M; (Old Westbury, NY) ; Lethif; Nizar;
(Croton-on-Hudson, NY) ; Song; Naihui; (New York,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
59235523 |
Appl. No.: |
14/983629 |
Filed: |
December 30, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/151 20200101;
G06F 17/18 20130101; G06F 40/123 20200101; G06F 17/10 20130101;
G06F 40/134 20200101; G06F 40/30 20200101; G06Q 10/20 20130101 |
International
Class: |
G06F 17/27 20060101
G06F017/27; G06F 17/22 20060101 G06F017/22; G06F 3/0482 20060101
G06F003/0482; G06F 3/0484 20060101 G06F003/0484; G06F 17/18
20060101 G06F017/18 |
Claims
1. A method for an ontology-guided graphical user interface to an
ontology based analytics solution library comprising: associating
the ontology-guided graphical user interface with the ontology
based analytics solution library having one or more analytics
families, each of the one or more analytics families comprising one
or more analytics, each of the one or more analytics comprising one
or more algorithms, and each of the one or more algorithms
comprising one or more use cases, wherein the ontology based
analytics solution library is configured to collect input data,
perform analysis of the input data using the one or more
algorithms, generate one or more solutions based on the results of
the analysis of the input data, and store the one or more solutions
generated in the analytics solution library; receiving a definition
of an asset management problem to be solved using the ontology
based analytics solution library from a user via the
ontology-guided graphical user interface; navigating, by the user
via the ontology-guided graphical user interface, through the one
or more analytics families, the one or more analytics, the one or
more algorithms, and the one or more use cases, to locate one or
more solutions from the ontology based analytics solution library
according to the definition of the asset management problem;
building one or more workflows to solve the asset management
problem according to the one or more solutions located; and
executing the one or more workflows to solve the asset management
problem; wherein the navigating comprises: selecting an analytics
family from the one or more analytics families according to the
definition of the asset management problem; selecting an analytics
from the analytics family selected according to the definition of
the asset management problem; selecting an algorithm from the
analytics selected according to the definition of the asset
management problem; and selecting a use case from the algorithm
selected according to the definition of the asset management
problem.
2. The method of claim 1, wherein the asset management problem
comprises: one or more predictive maintenance problems including
maintenance planning of certain equipment, and maintenance
scheduling of buildings or assets; one or more predictive failure
analysis problems including equipment failure risk analysis, and
anomaly detection of certain assets; one or more process and
equipment analysis problems including survival analysis of
manufacturing equipment, and electrodeposition anomaly detection;
and one or more process monitoring and optimization problems
including certain equipment failure monitoring, and model
predicative control of industrial tools.
3. (canceled)
4. The method of claim 1, wherein the ontology-guided graphical
user interface comprises at least an analytics family view, an
analytics view, an algorithm view, a use case view and a
relationship view.
5. The method of claim 1, wherein the building one or more
workflows comprises generating the one or more workflows for the
analytics selected as an actionable widget.
6. The method of claim 1, wherein the executing comprises:
providing a uniform interface for storing the input data and
retrieving output data via a data store services module; providing
a generic execution environment for the algorithm selected via an
execution services module; implementing the algorithm selected via
an analytics services module; providing one or more visualizations
for the input data and the output data on a visualization device
via a visualization services module; and providing one or more
schematic and structural transformations for the input data and the
output data via a data transformation services module.
7. The method of claim 1, wherein the executing comprises a
representational state transfer (REST)-based wrapper service.
8. An analytics system comprising: a processor configured to
operate the analytics system having an ontology-guided graphical
user interface and an ontology based analytics solution library
having one or more analytics families, each of the one or more
analytics families comprising one or more analytics, each of the
one or more analytics comprising one or more algorithms, and each
of the one or more algorithms comprising one or more use cases,
wherein the ontology based analytics solution library is configured
to collect input data, perform analysis of the input data using the
one or more algorithms, generate one or more solutions based on the
results of the analysis of the input data, and store the one or
more solutions generated in the ontology based analytics solution
library; a uniform interface for storing the input data and
retrieving output data via a data store services module; an
execution services module for generating a generic execution
environment for an algorithm selected from the one or more
algorithms according to a definition of an asset management problem
to be solved by the analytics system from a user via the
ontology-guided graphical user interface; an analytics services
module for implementing the algorithm selected; a visualization
device for one or more visualizations for the input data and the
output data via a visualization services module; a data
transformation services module for generating one or more schematic
and structural transformations for the input data and the output
data; and a memory storing computer executable instructions which,
when executed at the processor of the analytics system, cause the
processor to perform: associating the ontology-guided graphical
user interface with the ontology based analytics solution library;
navigating, by the user via the ontology-guided graphical user
interface, through the one or more analytics families, the one or
more analytics, the one or more algorithms, and the one or more use
cases, to locate one or more from the ontology based analytics
solution library according to the definition of the asset
management problem; building one or more workflows to solve the
asset management problem according to the one or more solutions
located; and executing the one or more workflows to solve the asset
management problem; wherein the navigating comprises: selecting an
analytics family from the one or more analytics families according
to the definition of the asset management problem; selecting an
analytics from the analytics family selected according to the
definition of the asset management problem; selecting an algorithm
from the analytics selected according to the definition of the
asset management problem; and selecting a use case from the
algorithm selected according to the definition of the asset
management problem.
9. The analytics system of claim 8, wherein the asset management
problem comprises: one or more predictive maintenance problems
including maintenance planning of certain equipment, and
maintenance scheduling of buildings or assets; one or more
predictive failure analysis problems including equipment failure
risk analysis, and anomaly detection of certain assets; one or more
process and equipment analysis problems including survival analysis
of manufacturing equipment, and electrodeposition anomaly
detection; and one or more process monitoring and optimization
problems including certain equipment failure monitoring, and model
predicative control of industrial tools.
10. (canceled)
11. The analytics system of claim 8, wherein the ontology-guided
graphical user interface comprises at least an analytics family
view, an analytics view, an algorithm view, a use case view and a
relationship view.
12. The analytics system of claim 8, wherein the building comprises
generating the one or more workflows for the analytics selected as
an actionable widget.
13. (canceled)
14. The analytics system of claim 10, wherein the executing
comprises a representational state transfer (REST)-based wrapper
service.
15. A non-transitory computer storage medium having computer
executable instructions stored thereon which, when executed by a
processor of an analytics system, cause the processor to perform:
associating an ontology-guided graphical user interface with an
ontology based analytics solution library having one or more
analytics families, each of the one or more analytics families
comprising one or more analytics, each of the one or more analytics
comprising one or more algorithms, and each of the one or more
algorithms comprising one or more use cases, wherein the ontology
based analytics solution library is configured to collect input
data, perform analysis of the input data using the one or more
algorithms, generate one or more solutions based on the results of
the analysis of the input data, and store the one or more solutions
generated in the ontology based analytics solution library;
receiving a definition of an asset management problem to be solved
by the analytics system from a user via the ontology-guided
graphical user interface; navigating, by the user via the
ontology-guided graphical user interface, through the one or more
analytics families, the one or more analytics, the one or more
algorithms, and the one or more use cases, to locate one or more
solutions from the ontology based analytics solution library
according to the definition of the asset management problem;
building one or more workflows to solve the asset management
problem according to the one or more solutions located; and
executing the one or more workflows to solve the asset management
problem; wherein the navigating comprises: selecting an analytics
family from the one or more analytics families according to the
definition of the asset management problem; selecting an analytics
from the analytics family selected according to the definition of
the asset management problem; selecting an algorithm from the
analytics selected according to the definition of the asset
management problem; and selecting a use case from the algorithm
selected according to the definition of the asset management
problem.
16. The non-transitory computer storage medium of claim 15, wherein
the asset management problem comprises: one or more predictive
maintenance problems including maintenance planning of certain
equipment, and maintenance scheduling of buildings or assets; one
or more predictive failure analysis problems including equipment
failure risk analysis, and anomaly detection of certain assets; one
or more process and equipment analysis problems including survival
analysis of manufacturing equipment, and electrodeposition anomaly
detection; and one or more process monitoring and optimization
problems including certain equipment failure monitoring, and model
predicative control of industrial tools.
17. (canceled)
18. The non-transitory computer storage medium of claim 15, wherein
the ontology-guided graphical user interface comprises at least an
analytics family view, an analytics view, an algorithm view, a use
case view and a relationship view.
19. The non-transitory computer storage medium of claim 15, wherein
the building comprises generating the one or more workflows for the
analytics selected as an actionable widget.
20. The non-transitory computer storage medium of claim 15, wherein
the executing comprises a representational state transfer
(REST)-based wrapper service including: providing a uniform
interface for storing the input data and retrieving output data via
a data store services module; providing a generic execution
environment for the algorithm selected via an execution services
module; implementing the algorithm selected via an analytics
services module; providing one or more visualizations for the input
data and the output data on a visualization device via a
visualization services module; and providing one or more schematic
and structural transformations for the input data and the output
data via a data transformation services module.
Description
BACKGROUND
[0001] The present disclosure relates generally to resource and
operations management, and more particularly to methods, systems
and computer program products of an analytics system having an
ontology-guided graphical user interface for analytics models.
[0002] Analytics is a multidimensional discipline. There is
extensive use of mathematics and statistics, the use of descriptive
techniques and predictive models to gain valuable knowledge from
data--data analysis. The insights from data are used to recommend
action or to guide decision making rooted in business context.
Thus, analytics is not so much concerned with individual analyses
or analysis steps, but with the entire methodology.
[0003] There are many types of analytics that can solve various
problems in industry in the areas of resource and operations
management, but it is often difficult for common users who don't
have in-depth knowledge of analytics available to select
appropriate analytics for solving a specific problem, develop and
run the analytics in appropriate steps and manners to produce
desired results. It is desirable to have a user interface that
enable the common users without specialized training in
mathematical modeling to identify, develop, and use analytics to
solve complex industrial problems in resource and operations
management.
[0004] Therefore, heretofore unaddressed needs still exist in the
art to address the aforementioned deficiencies and
inadequacies.
SUMMARY
[0005] In an embodiment of the present invention, a method of an
analytics system having an ontology-guided graphical user interface
for analytics models may include: linking the ontology-guided
graphical user interface for analytics models to an ontology based
analytics solution library, receiving a definition of a management
project to be solved by the analytics system from a user via the
ontology-guided graphical user interface for analytics models,
selecting, by the user via the ontology-guided graphical user
interface for analytics models, a solution from the ontology based
analytics solution library according to the management project,
building one or more workflows to solve the management project, and
executing the workflows to generate solution to the management
project. In certain embodiments, the building may include
generating the workflows of modeling tasks for the analytics
selected as an actionable widget. The executing may include a
representational state transfer (REST)-based wrapper service. In
exemplary embodiments, the REST-based wrapper service may include:
providing a uniform interface for storing input data and retrieving
output data via a data store services module, providing a generic
execution environment for the algorithm selected via an execution
services module, implementing the algorithm selected via an
analytics services module, providing a variety of visualizations
for input and output data on a visualization device via a
visualization services module, and providing a variety of schematic
and structural transformations for input data and output data via a
data transformation services module.
[0006] In another embodiment of the present invention, an analytics
system may include a computer. The computer may include a processor
to operate an ontology-guided graphical user interface for
analytics models, and a memory storing computer executable
instructions for the ontology-guided graphical user interface for
analytics models of the analytics system. When the computer
executable instructions are executed at the processor, the computer
executable instructions cause the analytics system to perform:
linking the ontology-guided graphical user interface for analytics
models to an ontology based analytics solution library, receiving a
definition of a management project to be solved by the analytics
system from a user via the ontology-guided graphical user interface
for analytics models, selecting, by the user via the
ontology-guided graphical user interface for analytics models, a
solution from the ontology based analytics solution library
according to the management project, building one or more workflows
to solve the management project, and executing the workflows to
generate solution to the management project. In certain
embodiments, the building may include generating the workflows of
modeling tasks for the analytics selected as an actionable
widget.
[0007] In yet another embodiment of the present invention, the
present disclosure relates to a non-transitory computer storage
medium. In certain embodiments, the non-transitory computer storage
medium stores computer executable instructions. When these computer
executable instructions are executed by a processor of an analytics
system having an ontology-guided graphical user interface for
analytics models, these computer executable instructions cause the
analytics system to perform: linking the ontology-guided graphical
user interface for analytics models to an ontology based analytics
solution library, receiving a definition of a management project to
be solved by the analytics system from a user via the
ontology-guided graphical user interface for analytics models,
selecting, by the user via the ontology-guided graphical user
interface for analytics models, a solution from the ontology based
analytics solution library according to the management project,
building one or more workflows to solve the management project, and
executing the workflows to generate solution to the management
project. In certain embodiments, the building may include
generating the workflows of modeling tasks for the analytics
selected as an actionable widget.
[0008] These and other aspects of the present disclosure will
become apparent from the following description of the preferred
embodiment taken in conjunction with the following drawings and
their captions, although variations and modifications therein may
be affected without departing from the spirit and scope of the
novel concepts of the disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The subject matter which is regarded as the invention is
particularly pointed out and distinctly claimed in the claims at
the conclusion of the specification. The foregoing and other
features and advantages of the invention are apparent from the
following detailed description taken in conjunction with the
accompanying drawings in which:
[0010] FIG. 1 is a block diagram illustrating an exemplary
processing system of an analytics system having an ontology-guided
analytics graphical user interface according to certain embodiments
of the present invention;
[0011] FIG. 2 is an exemplary ontology hierarchy of assets
management for the ontology-guided graphical user interface for
analytics models according to certain embodiments of the present
invention;
[0012] FIG. 3 is an exemplary smarter resource and operation
management (SROM) industry solution library for the ontology-guided
graphical user interface for analytics models according to certain
embodiments of the present invention;
[0013] FIG. 4 is a block diagram illustrating an exemplary
ontology-guided graphical user interface for analytics models
according to certain embodiments of the present invention; and
[0014] FIG. 5 is a flow chart of an exemplary method of the
ontology-guided graphical user interface for analytics models of
the analytics system according to certain embodiments of the
present invention.
DETAILED DESCRIPTION
[0015] The present disclosure is more particularly described in the
following examples that are intended as illustrative only since
numerous modifications and variations therein will be apparent to
those skilled in the art. Various embodiments of the disclosure are
now described in detail. Referring to the drawings, like numbers,
if any, indicate like components throughout the views. As used in
the description herein and throughout the claims that follow, the
meaning of "a", "an", and "the" includes plural reference unless
the context clearly dictates otherwise. Also, as used in the
description herein and throughout the claims that follow, the
meaning of "in" includes "in" and "on" unless the context clearly
dictates otherwise. Moreover, titles or subtitles may be used in
the specification for the convenience of a reader, which shall have
no influence on the scope of the present disclosure. Additionally,
some terms used in this specification are more specifically defined
below.
[0016] The terms used in this specification generally have their
ordinary meanings in the art, within the context of the disclosure,
and in the specific context where each term is used. Certain terms
that are used to describe the disclosure are discussed below, or
elsewhere in the specification, to provide additional guidance to
the practitioner regarding the description of the disclosure. It
will be appreciated that same thing can be said in more than one
way. Consequently, alternative language and synonyms may be used
for any one or more of the terms discussed herein, nor is any
special significance to be placed upon whether or not a term is
elaborated or discussed herein. The use of examples anywhere in
this specification including examples of any terms discussed herein
is illustrative only, and in no way limits the scope and meaning of
the disclosure or of any exemplified term. Likewise, the disclosure
is not limited to various embodiments given in this
specification.
[0017] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this disclosure pertains. In the
case of conflict, the present document, including definitions will
control.
[0018] As used herein, "plurality" means two or more. The terms
"comprising," "including," "carrying," "having," "containing,"
"involving," and the like are to be understood to be open-ended,
i.e., to mean including but not limited to.
[0019] The term computer program, as used above, may include
software, firmware, and/or microcode, and may refer to programs,
routines, functions, classes, and/or objects. The term shared, as
used above, means that some or all code from multiple modules may
be executed using a single (shared) processor.
[0020] Analytics is the discovery and communication of meaningful
patterns in data. Especially valuable in areas rich with recorded
information, analytics relies on the simultaneous application of
statistics, computer programming and operations research to
quantify performance.
[0021] The term "SROM" stands for smarter resource and operation
management.
[0022] The term "REST" stands for representational state
transfer.
[0023] The term "GUI" stands for graphical user interface.
[0024] "Spark" is an open source cluster computing framework.
[0025] "R" a programming language and software environment for
statistical computing and graphics supported by the R Foundation
for Statistical Computing..sup.I
[0026] "C++" is a general-purpose programming language.
[0027] "Python" is a widely used general-purpose, high-level
programming language.
[0028] "ILOG CPLEX" is an optimization studio for development and
deployment of optimization models, combining leading solver engines
with a tightly integrated IDE and modeling language.
[0029] "SPSS" stands for Statistical Package for the Social
Sciences, which is a software package used for statistical
analysis.
[0030] "HDFS" stands for Hadoop Distributed File System, which is a
distributed, scalable, and portable file-system written in Java for
the Hadoop framework.
[0031] The apparatuses and methods described herein may be
implemented by one or more computer programs executed by one or
more processors. The computer programs include processor-executable
instructions that are stored on a non-transitory tangible computer
readable medium. The computer programs may also include stored
data. Non-limiting examples of the non-transitory tangible computer
readable medium are nonvolatile memory, magnetic storage, and
optical storage.
[0032] The present disclosure will now be described more fully
hereinafter with reference to the accompanying drawings FIGS. 1-5,
in which certain exemplary embodiments of the present disclosure
are shown. The present disclosure may, however, be embodied in many
different forms and should not be construed as limited to the
embodiments set forth herein; rather, these embodiments are
provided so that this disclosure will be thorough and complete, and
will fully convey the scope of the disclosure to those skilled in
the art.
[0033] Referring to FIG. 1, there is shown an embodiment of an
analytics system 100 for implementing an ontology-guided analytics
graphical user interface herein. In this embodiment, the analytics
system 100 has one or more central processing units (processors)
101a, 101b, 101c, etc. (collectively or generically referred to as
processor(s) 101). In one embodiment, each processor 101 may
include a reduced instruction set computer (RISC) microprocessor.
Processors 101 are coupled to system memory 114 and various other
components via a system bus 113. Read only memory (ROM) 102 is
coupled to the system bus 113 and may include a basic input/output
system (BIOS), which controls certain basic functions of the
analytics system 100.
[0034] FIG. 1 further depicts an input/output (I/O) adapter 107 and
a network adapter 106 coupled to the system bus 113. I/O adapter
107 may be a small computer system interface (SCSI) adapter that
communicates with a hard disk 103 and/or tape storage drive 105 or
any other similar component. I/O adapter 107, hard disk 103, and
tape storage device 105 are collectively referred to herein as mass
storage 104. Operating system 120 for execution on the analytics
system 100 may be stored in mass storage 104. A network adapter 106
interconnects bus 113 with an outside network 116 enabling the
analytics system 100 to communicate with other such systems, for
example, an external input, output, training database 440 as shown
in FIG. 4. A screen (e.g., a display monitor) 115 is connected to
system bus 113 by display adaptor 112, which may include a graphics
adapter to improve the performance of graphics intensive
applications and a video controller. The ontology-guided analytics
graphical user interface of the analytics system 100 may be
displayed on the screen 115. In one embodiment, adapters 107, 106,
and 112 may be connected to one or more I/O busses that are
connected to system bus 113 via an intermediate bus bridge (not
shown). Suitable I/O buses for connecting peripheral devices such
as hard disk controllers, network adapters, and graphics adapters
typically include common protocols, such as the Peripheral
Component Interconnect (PCI). Additional input/output devices are
shown as connected to system bus 113 via user interface adapter 108
and display adapter 112. A keyboard 109, mouse 110, and speaker 111
all interconnected to bus 113 via user interface adapter 108, which
may include, for example, a Super I/O chip integrating multiple
device adapters into a single integrated circuit.
[0035] In exemplary embodiments, the analytics system 100 includes
a graphics processing unit 130. Graphics processing unit 130 is a
specialized electronic circuit designed to manipulate and alter
memory to accelerate the creation of images in a frame buffer
intended for output to a display. In general, graphics processing
unit 130 is very efficient at manipulating computer graphics and
image processing, and has a highly parallel structure that makes it
more effective than general-purpose CPUs for algorithms where
processing of large blocks of data is done in parallel.
[0036] Thus, as configured in FIG. 1, the analytics system 100
includes processing capability in the form of processors 101,
storage capability including system memory 114 and mass storage
104, input means such as keyboard 109 and mouse 110, and output
capability including speaker 111 and display 115. In one
embodiment, a portion of system memory 114 and mass storage 104
collectively store an operating system to coordinate the functions
of the various components shown in FIG. 1. In certain embodiments,
the network 116 may include symmetric multiprocessing (SMP) bus, a
Peripheral Component Interconnect (PCI) bus, local area network
(LAN), wide area network (WAN), telecommunication network, wireless
communication network, and the Internet.
[0037] Referring now to FIG. 2, an exemplary ontology hierarchy of
assets management for the ontology-guided graphical user interface
for analytics models is shown according to certain embodiments of
the present invention. In certain embodiments, the ontology
hierarchy of assets management may include a set of analytics
families. In one embodiment, the analytics families may include, as
shown, Predictive Maintenance analytics family, Predictive Failure
Analysis analytics family, Process and Equipment Analysis analytics
family, Process Monitoring and Optimization analytics family, and
etc.
[0038] Each of the set of analytics families may include a set of
analytics. For example, the Predictive Maintenance analytics family
may include Maintenance Planning analytics and Maintenance
Scheduling analytics. The Predictive Failure Analysis analytics
family may include Failure Pattern Analysis analytics, and Failure
Risk Analysis analytics. The Process and Equipment Analysis
analytics family may include Anomaly Detection analytics and Fault
Detection and Diagnosis analytics. The Process Monitoring and
Optimization analytics family may include Process Optimization
analytics and Model Predictive Control analytics. There may be more
analytics available for analytics families not listed here.
[0039] In certain embodiments, each of the analytics may include a
set of algorithms. For example, each of the Maintenance Planning
analytics and the Maintenance Scheduling analytics may include
Mixed Integer Programming (MIP) algorithm, Non-Linear Programming
(NLP) algorithm, and Dynamic Programming (DP) algorithm. Each of
the Failure Pattern Analysis analytics, the Failure Risk Analysis
analytics, the Anomaly Detection analytics, the Fault Detection and
Diagnosis analytics, the Process Optimization analytics, and the
Model Predictive Control analytics may include one or more
algorithms such as: Random Forest algorithm, Support Vector Machine
algorithm, Semi-Parametric Analysis algorithm, Parametric Analysis
algorithm, Graphical Method algorithm, Hidden Markov Model
algorithm, and Autoregressive Neural Network algorithm etc.
[0040] In certain embodiments, each of the algorithms may include a
set of use cases. For example, each of the MIP algorithm, the NLP
algorithm, the DP algorithm, the Random Forest algorithm, the
Support Vector Machine algorithm, the Semi-Parametric Analysis
algorithm, the Parametric Analysis algorithm, the Graphical Method
algorithm, the Hidden Markov Model algorithm may include one or
more use cases such as Maintenance Planning of Transformer,
Maintenance Scheduling of Oil Well, Semiconductor Tool Failure Risk
Analysis, Anomaly Detection of Mining Machinery, Offshore Oil
Platform Anomaly Detection, Survival Analysis of Semiconductor
Manufacturing Equipment, Electrodeposition Anomaly Detection,
Railcar Component Failure Monitoring, and Semiconductor Process
Anomaly Detection.
[0041] Referring new to FIG. 3, an exemplary smarter resource and
operation management (SROM) industry solution library for the
ontology-guided graphical user interface for analytics models is
shown according to certain embodiments of the present invention.
The ontology of SROM solution may include a variety of analytics
views, algorithm views, use case views and relationship views. FIG.
3 shows a relationship view among the various input data, analytics
solution library, and state-of-the-art algorithms. The input data
may come from Internet of Things (IoT). The input data may include
Asset and Equipment Attributes, Failure/Repair History, Operations
Data, Process Data, Product Attributes, Environmental Data (e.g.
weather), Sensors and Devices, Meters, Grid Energy Price,
Resources, Costs and Budget etc.
[0042] In certain embodiments, the analytics solution library may
include one or more of analytics such as: Maintenance Planning
analytics, Maintenance Scheduling analytics, Failure Risk Analysis
of Assets analytics, Failure Pattern Analysis of Assets analytics,
Anomaly Detection analytics, Fault Detection and Diagnosis
analytics, Process Optimization analytics, and Model Predictive
Control analytics etc.
[0043] In certain embodiments, the state-of-the-art algorithms may
include one or more algorithms such as MIP algorithm, the NLP
algorithm, the DP algorithm, the Random Forest algorithm, the
Support Vector Machine algorithm, Neural Network Algorithm, the
Semi-Parametric Analysis algorithm, the Parametric Analysis
algorithm, the Graphical Method algorithm, Cohort Analysis
algorithm, the Hidden Markov Model algorithm etc.
[0044] Referring now to FIG. 4, a block diagram illustrating an
exemplary ontology-guided graphical user interface 400 for
analytics models is shown according to certain embodiments of the
present invention. In certain embodiments, ontology-guided
graphical user interface 400 for analytics models may include,
among other things, an ontology-based analytics solution library
module 410, a user project input module 420, and a workflow module
430.
[0045] In certain embodiments, the ontology-based analytics
solution library module 410 may include a set of analytics
families. For example, the set of analytics families may include:
Predictive Maintenance Analytics Family, Predictive Failure
Analysis Analytics Family, and Process Monitoring and Optimization
Analytics Family, etc.
[0046] Each of the set of analytics families may include a set of
analytics. For example, the predictive maintenance analytics family
may include analytics such as maintenance planning analytics, and
maintenance scheduling analytics. The predictive failure analysis
analytics family may include failure pattern analysis analytics,
and failure risk analysis analytics. A process and equipment
analysis analytics family may include anomaly detection
analytics.
[0047] Each of the set of analytics may include a set of
algorithms. For example, the maintenance planning analytics of the
predictive maintenance analytics family may include mathematical
programming algorithm. The failure risk analysis analytics of the
predictive failure analysis analytics family may include a
parametric analysis algorithm. The anomaly detection analytics of
the process and equipment analysis analytics family may include:
graphical methods-outlier analysis algorithm, graphical
methods-sliding window analysis algorithm, graphical methods-data
set comparison algorithm, graphical methods-multiple comparison
algorithm, hidden Markov method algorithm, ensemble method
algorithm, and statistical learning algorithm.
[0048] Each of the set of algorithms may include a set of use
cases. For example, the mathematical programming algorithm may
include maintenance planning of transformer use case. The
parametric analysis algorithm may include water main failure
prediction use case, and railcar components failure risk monitoring
use case. An autoregressive neural network based algorithm of the
model predictive control of process analytics may include optimal
control of HVAC system use case.
[0049] The analytics families, the analytics, the algorithms, and
the use cases listed above are only a tiny fraction of the
analytics families, the analytics, the algorithms, and the use
cases available, and these listing are not meant to be exhaustive.
Additional analytics families, analytics, algorithms, and use cases
may be added to the ontology-based analytics solution library
module 410 as these analytics families, analytics, algorithms, and
use cases become available.
[0050] In certain embodiments, the user project input module 420
allows a user to define a project, or problem. The user uses the
user project input module 420 to enter name, description, nature,
and purpose of the project, to specify locations of input data and
training data, to specify analytics model to use, to select display
methods for input data, training data, intermediate data, results,
and final solutions to the project, etc.
[0051] In certain embodiments, the workflow module 430 may include
a Data Store Services Module 431, a Data Transformation Services
Module 432, an Analytics Services Module 433, an Execution Services
Module 434, and a Visualization Services Module 435.
[0052] The Data Store Services Module 431 may provide a uniform
interface for storing input data and retrieving output data. Based
on the analytics, the backing data store could be a local file
system, a distributed file system like HDFS or a database like
DB2/Cassandra. The Data Store Services Module 431 may be connected
to an input, output and training database 440 to data
exchanges.
[0053] The Data Transformation Services Module 432 may provide
various schematic/structural transformations for input/output data.
For example, if the user has data in csv format, and the algorithm
accepts data in json (JavaScript Object Notation, a lightweight
data-interchange format), there can be a service which transforms
the data from csv to json format. This can be extended to provide
other transformations.
[0054] The Analytics Services Module 433 may provide the
implementation of the algorithm for a given analytics and use other
services like the Data Store Services Module 431 and the Execution
Services Module 434 to perform its action.
[0055] The Execution Services Module 434 may provide a generic
execution environment for the algorithm. The Analytics Services
Module 433 may use this service to execute the algorithm. Examples
of the generic execution environment for the algorithm may include
Spark, Matlab, R, C++, Python, ILOG Cplex etc.
[0056] The Visualization Services Module 435 may provide various
visualizations (graphs, tables etc) for input and output data. The
Visualization Services Module 435 may be specific to a particular
input/output data format, and can be extended to add more GUI
widgets. The Visualization Services Module 435 may be connected to
other visualization device 450 for display on such device.
[0057] In certain embodiments, once the user defines the project,
the user uses the ontology-guided graphical user interface 400 to
select an analytics model. In one embodiment, the user selects an
analytics family, an analytics, an algorithm and a use case. In
another embodiment, the user selects only an analytics family and
an analytics. For example, the user may click "Predictive Failure
Analysis" in an Analytics Family view, "Failure Risk Analysis" in
an Analytics view, "Semi-Parametric Analysis" in an Algorithm view,
and "Failure Risk Analysis of Semiconductor" in a use case view.
Then, the user may press "ESC" key to end the selection.
[0058] In one embodiment, once the analytics is selected, the
workflow module 430 of the ontology-guided graphical user interface
400 may generate workflow of 9 steps as actionable widget (e.g. a
clickable button on the ontology-guided graphical user interface
400 for analytics models). The workflows generated may include:
[0059] (1). Invoking the Data Store Services Module 431 and the
Data Transformation Services Module 432 for uploading maintenance
data file;
[0060] (2). Invoking the Data Store Services Module 431 and the
Data Transformation Services Module 432 for uploading process
history data;
[0061] (3). Invoking the Data Store Services Module 431 and the
Data Transformation Services Module 432 for uploading process
operations data;
[0062] (4). Invoking the Analytics Services Module 433 and the
Execution Services Module 434 for prepare Cox Regression Table;
[0063] (5). Invoking the Analytics Services Module 433 using
Semi-Parametric Risk Analysis Model and the Execution Services
Module 434 using Matlab to compute and display Survival/Failure
Function;
[0064] (6). Invoking the Visualization Services Module 435 for
displaying Survival/Failure Function by Replacement Reason;
[0065] (7). Invoking the Visualization Services Module 435 using D3
graphic library for displaying Survival/Failure Function by
Replacement Part Condition;
[0066] (8). Invoking the Analytics Services Module 433 using
Feature selection algorithm and the Execution Services Module 434
using Statistical Package for the Social Sciences (SPSS) for
Feature Selection for Cox-Regression; and
[0067] (9). Invoking the Analytics Services Module 433 using
Semi-Parametric Risk Analysis Model and the Execution Services
Module 434 using Matlab for Parametric Analysis.
[0068] Referring now to FIG. 5, a flow chart of an exemplary method
500 of the ontology-guided graphical user interface for analytics
models of the analytics system is shown according to certain
embodiments of the present invention.
[0069] As shown at block 502, the ontology-guided graphical user
interface 400 may link to a knowledge base. In certain embodiments,
the knowledge base may be an ontology-based analytics solution
library module 410. As described in earlier section, the
ontology-based analytics solution library module 410 may include a
hierarchy of analytics, formed in a shape of a tree. For example,
the ontology-based analytics solution library module 410 may
include many different analytics families. Each of the analytics
families may include many different analytics. Each of the
different analytics may include many different algorithms. Each of
the algorithms may include many different use cases. The
ontology-based analytics solution library module 410 is organized
in such way that enables a user without specialized training in
mathematical modeling to navigate through a collection of diverse
analytics, develop, and use analytics to solve complex industrial
problems in resource and operations management.
[0070] At block 504, the user uses the user project input module
420 of the ontology-guided graphical user interface 400 to define a
project to be solved by the analytics system 100. In certain
embodiments, the user uses the user project input module 420 to
enter name, description, nature, and purpose of the project, to
specify locations of input data and training data, to specify
analytics model to use, to select display methods for input data,
training data, intermediate data, results, and final solutions to
the project.
[0071] At block 506, the user uses the user project input module
420 of the ontology-guided graphical user interface 400 to select a
solution to the project. In certain embodiments, the user uses the
ontology-guided graphical user interface 400 to select an analytics
model. For example, the user may click "Process and Equipment
Analysis" in an Analytics Family view, "Anomaly Detection" in an
Analytics view, "Graphical Method" using Outlier Analysis algorithm
in an Algorithm view, and "Mining Machinery" in a use case view.
Then, the user may press "ESC" key to end the selection.
[0072] At block 508, the workflow module 430 may then generate
workflows based on the selection in block 506. In one embodiment,
the workflow module 430 may then generate workflow of 5 Steps as
actionable widgets (e.g., clickable buttons etc.) The actionable
widgets may include:
[0073] (1). Invoking the Data Store Services Module 431 and the
Data Transformation Services Module 432 for uploading training data
file;
[0074] (2). Invoking the Analytics Services Module 433 using
graphical method algorithm to build Detection Model;
[0075] (3). Invoking the Data Store Services Module 431 and the
Data Transformation Services Module 432 for uploading local
validation data file;
[0076] (4). Invoking the Analytics Services Module 433 and the
Execution Services Module 434 using R/Python to validate the
models, and invoking the Visualization Services such as D3 graphic
library to view Anomaly & Dependency Graph; and
[0077] (5). Invoking the Execution Services Module 434 using
R/Python to calculate Prediction Accuracy, and invoking the
Visualization Services such as D3 graphic library to set
threshold.
[0078] At block 510, the workflow module 430 may execute the
workflows generated in the block 508. In certain embodiments, the
executing may include a representational state transfer
(REST)-based wrapper service. In exemplary embodiments, the
REST-based wrapper service may include: providing a uniform
interface for storing input data and retrieving output data via the
Data Store Services Module 431, providing a generic execution
environment for the algorithm selected via the Execution Services
Module 434, implementing the algorithm selected via the Analytics
services module 433, providing a variety of visualizations for
input and output data on a visualization device via the
Visualization Services Module 435, and providing a variety of
schematic and structural transformations for input data and output
data via the Data Transformation Services Module 432.
[0079] In another embodiment of the present invention, an analytics
system may include a computer. The computer may include a processor
to operate an ontology-guided graphical user interface for
analytics models, and a memory storing computer executable
instructions for the ontology-guided graphical user interface for
analytics models of the analytics system. When the computer
executable instructions are executed at the processor, the computer
executable instructions cause the analytics system to perform:
linking the ontology-guided graphical user interface for analytics
models to an ontology based analytics solution library, receiving a
definition of a management project to be solved by the analytics
system from a user via the ontology-guided graphical user interface
for analytics models, selecting, by the user via the
ontology-guided graphical user interface for analytics models, a
solution from the ontology based analytics solution library
according to the management project, building one or more workflows
to solve the management project, and executing the workflows to
generate solution to the management project.
[0080] In yet another embodiment of the present invention, the
present disclosure relates to a non-transitory computer storage
medium. In certain embodiments, the non-transitory computer storage
medium stores computer executable instructions. When these computer
executable instructions are executed by a processor of an analytics
system having an ontology-guided graphical user interface for
analytics models, these computer executable instructions cause the
analytics system to perform: linking the ontology-guided graphical
user interface for analytics models to an ontology based analytics
solution library, receiving a definition of a management project to
be solved by the analytics system from a user via the
ontology-guided graphical user interface for analytics models,
selecting, by the user via the ontology-guided graphical user
interface for analytics models, a solution from the ontology based
analytics solution library according to the management project,
building one or more workflows to solve the management project, and
executing the workflows to generate solution to the management
project.
[0081] The present invention may be a computer system, a method,
and/or a computer program product. The computer program product may
include a computer readable storage medium (or media) having
computer readable program instructions thereon for causing a
processor to carry out aspects of the present invention.
[0082] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0083] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0084] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0085] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, and computer program products according to embodiments of
the invention. It will be understood that each block of the
flowchart illustrations and/or block diagrams, and combinations of
blocks in the flowchart illustrations and/or block diagrams, can be
implemented by computer readable program instructions.
[0086] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0087] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0088] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0089] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *